RECURRENCE QUANTIFICATION ANALYSIS OF ELECTROCHEMICAL NOISE DATA DURING PIT DEVELOPMENT

Abstract

Electrochemical noise (EN) data is commonly used to monitor corrosion of metals in various environments. In this work we use recurrence quantification analysis (RQA) to study EN time series of stainless steel AISI 316 samples immersed in a mildly corrosive electrolyte. It is found that RQA of current and potential time series reveal different information: current time series provides detailed information on the kinetics of the pitting corrosion process, while the potential time series identifies the transitions from one thermodynamic state to another in the pitting corrosion process.